Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings were received on 12/10/2024. These drawings are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2,3 recites the limitation "a record" in claim 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 7 recites the limitation "the given level" in claims 1,6. There is insufficient antecedent basis for this limitation in the claim.
Claim 10 recites the limitation "the respective column" in claim 1. There is insufficient antecedent basis for this limitation in the claim. Note: Limitation “mapping a plurality of vertices of the structure graph to respective columns …” precedes “the respective column. Is the limitation referencing “respective columns” of previous limitation or is the limitation “a respective column of the respective columns”?
Claim 16 recites the limitation "the given level" in claims 111,15. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "the respective column" in claim 11. There is insufficient antecedent basis for this limitation in the claim. Note: Limitation “mapping a plurality of vertices of the structure graph to respective columns …” precedes “the respective column. Is the limitation referencing “respective columns” of previous limitation or is the limitation “a respective column of the respective columns”?
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1,4,5,11,13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al (US Patent No.: 10452700) in view of Maroo et al (US Publication No.: 20210182606), further in view of Mwarabu et al (US Publication No.: 20200342053).
Claim 1, Truong et al discloses
At training time (Fig. 3 shows training period):
Obtaining a plurality of training records (Fig. 3, label 302) for respective text documents (Col. 9, lines 47-55 discloses unstructured data (training records) from documents are stored in 302, wherein unstructured data can be log files (documents).), each of the training records having one or more input features representing unstructured content of the respective text document (Col. 9, lines 67-54,62-67, Col. 10, lines 1-6 discloses label 302 stores unstructured data such as log files, label 310 classifies the unstructured data into a type or category of the unstructured data. The unstructured data may include log files of a particular category or type. The category or type of the unstructured data is considered input feature representing unstructured content of the respective text document.); and
Training a machine learning classifier on the training records (Fig. 3, label 312 selects a neural network and outputs the selected neural network for training at label 316, trained on the unstructured data output from label 310. The unstructured data is generated by the classifier classifying unstructured data from the training records, label 302.); and
At run time (Fig. 7 shows run time or inference):
Obtaining a record representing unstructured content of an input document (Fig. 7, label 702); and
Inputting the record to the trained machine learning classifier (Fig. 7, label 708) to determine a structure graph of the input document (Fig. 7, label 708,710. Col. 18, lines 26-31 discloses the structured data may comprise at least one of relational data, graphical data or object-oriented data.).
Truong et al discloses training data comprising unstructured data (Fig. 3, label 302,310), but fails to disclose the training data having, as training labels, one or more training graphs describing relationships among the unstructured content of the respective text document.
Maroo et al discloses training a neural network (Fig. 1, label 33), wherein the training data (Fig. 1, label 10), used for training the neural network (Fig. 1, label 10,33), has, as training labels (Fig. 1, label KG as the knowledge graph. Paragraph 15 discloses the knowledge graph captures meaningful data associations and/or key text phrases to organize the unstructured data into high quality labeled dataset.), one or more training graphs describing relationships among the unstructured content of the respective text document (Fig. 1, label KG as the one or more training graphs describing relationships among the unstructured content of the respective text document, such as label 4,6).
Truong et al discloses training data comprising unstructured data and Maroo et al discloses training data comprising training graph as training labels to indicate the relationship between terms, hence it would be obvious to one skilled in the art before the effective filing date of the application to modify Truong et al’s training data to include training data comprising training graph as training labels as disclosed by Maroo et al so to improve the organization of the unstructured data in the training data and improve the training of a learning algorithm with labeled training data.
Truong et al discloses the neural network generates structured data such as graphical data (Col. 18, lines 26-31), but fails to disclose the graphical data the following wherein given level of the trained machine learning classifier has inputs representing a number N of tokens of the input document and has outputs representing N(N-1)/2 pairwise combinations of the N tokens, one or more of the outputs defining edges of the structure graph.
Mwarabu et al discloses using a neural network to generate structured data from unstructured data (Fig. 2, label unstructured natural language content (NLC), MC classified parse tree) wherein given level of the trained machine learning classifier has inputs representing a number N of tokens of the input document and has outputs representing N(N-1)/2 pairwise combinations of the N tokens, one or more of the outputs defining edges of the structure graph (Fig. 5 shows the classified parse tree representation output from the neural network shown in Fig. 2. The machine learning classifier or neural network receiving N tokens such as sentence “She understands that whole breast radiation will be recommended” (paragraph 56) and outputs N(N-1)/2 pairwise combinations of the N tokens with defining edges of the structure graph.). It would be obvious to one skilled in the art before the effective filing date of the application for the graphical data disclosed by Truong et al to include tokens and edges as disclosed by Mwarabu et al so to depict relationships between tokens as displayed via a graph.
Claim 4, Mwarabu et al discloses wherein the edges comprise: a first directed edge indicating a key-value relationship between a first pair of the tokens (Fig. 5, label key shows the key, value is the slot name. The edges show the relationship pair of tokens.); and a second undirected edge indicating a peer relationship between a second pair of the tokens (Fig. 5, label 510 has a relationship with 520 as well as a relationship shown in 510.).
Claim 5, Mwarabu et al discloses wherein the edges comprise three or more undirected edges forming a cycle (Fig. 5, label 500 shows undirected edges forming a cycle, such as the triangle connected to 510 and 510 connected to 520.).
Claim 6, Marco et al discloses wherein the one or more training graphs describe a hierarchy of relationships among two or more levels of entities of the respective text document (Fig. 1, label knowledge graph indicates a hierarchical relationship among entities of the unstructured data. Paragraph 23 discloses an example or embodiment of a knowledge graph includes representation of unstructured data such as common symptoms and possible symptoms, wherein the knowledge graph can include more than one sub knowledge graph, indicating more than 1 relationships (two or more levels of entities of the respective text document).).
Claim 11 recites similar limitations as claim 1 and is rejected on the same grounds as claim 1.
Claim 13 recites similar limitations as claim 4 and is rejected on the same grounds as claim 4.
Claim 14 recites similar limitations as claim 5 and is rejected on the same grounds as claim 5.
Claim 15 recites similar limitations as claim 6 and is rejected on the same grounds as claim 6.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al (US Patent No.: 10452700) in view of Maroo et al (US Publication No.: 20210182606), further in view of Mwarabu et al (US Publication No.: 20200342053) and further in view of Banerjee et al (US Publication No.: 20210200936)
Claim 2, Truong et al discloses the obtaining a record () comprises preprocessing an original form of the input document (Col. 2, lines 54-67 discloses preprocessing the original document.), but fails to disclose Recognizing at least one field in the input document as a named entity; and replacing the at least one field in the input document with the named entity.
Banerjee et al discloses one or more documents are processed by recognizing at least one field in the input document as a named entity (Paragraph 62 discloses identifying named entity such as Tenant A in the document. The field is Tenant A in the document, where name of tenant A is a supplier name (named entity).); and replacing the at least one field in the input document with the named entity (Paragraph 62 discloses replacing Tenant A such as “Acme Co” with the named entity “Supplier Name”.).
It would be obvious to one skilled in the art before the effective filing date of the application to modify the preprocessing the records as disclosed by Truong et al by recognizing and replacing at least one field with the named entity as disclosed by Banerjee et al so to improve anonymity of sensitive information and prepare the document for further processing such as classification.
Claim(s) 3,12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al (US Patent No.: 10452700) in view of Maroo et al (US Publication No.: 20210182606), further in view of Mwarabu et al (US Publication No.: 20200342053) and further in view of Bohn et al (US Publication No.: 20080082520).
Claim 3, Truong et al discloses the obtaining a record (Fig. 7, label 702. Col. 2, lines 54-67 discloses preprocessing the original document.), but fails to disclose obtaining the record comprises: tagging the record with positional encoding information.
Bohn et al discloses processing a document including tagging a record with positional encoding information (Paragraph 29 discloses tokenization (preprocessing of a document) includes tags such as positional encodings.). It would be obvious to one skilled in the art before the effective filing date of the application to modify Truong et al’s preprocessing of a document, hence obtaining a record, by incorporating tokenization including tags such as positional encodings as disclosed by Bohn et al so to prepare the document for further processing, hence improving further processing of a document.
Claim 12 recites similar limitations as claim 3 and is rejected on the same grounds as claim 3.
Claim(s) 10,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Truong et al (US Patent No.: 10452700) in view of Maroo et al (US Publication No.: 20210182606), further in view of Mwarabu et al (US Publication No.: 20200342053) and further in view of Bozkaya et al (US Publication No.: 20190278771)
Claim 10, Truong et al discloses the structure graph generated by the trained machine learning classifier (Fig. 7, label 708,710. Col. 18, lines 26-31 discloses the structured data may comprise at least one of relational data, graphical data or object-oriented data.), but fails to recite the limitations of claim 10.
Bozkaya et al discloses
mapping a plurality of vertices of the structure graph to respective columns of a database (Paragraph 32 discloses a set or nodes or vertices of the hypergraph represent columns of data tables stored in a database.);
adding one or more records to the database for the input document (Paragraph 32 discloses storage of subcontexts to specify relationships of data within a particular domain (document). The subcontexts are records in the database.); and
for each of the mapped vertices, storing a value representing content of the input document in the respective column of the added one or more records (Fig. 1, label 102,104,106 shows the tables and connection between tables. The vertices mapped to columns is “date_id”, “manufacture_country_code”, etc. Such labels of the vertices are considered value representing content of the input data in the respective column of the added one or more records (subcontexts).).
It would be obvious to one skilled in the art before the effective filing date of the application to modify Truong et al’s structure graph by storing the structure graph as disclosed by Bozkaya et al so to allow for easy access to the structure graph’s information, improving any further processing requiring the structure graph.
Allowable Subject Matter
Claims 7-9,16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 20 is allowed over prior art.
Conclusion
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/LINDA WONG/Primary Examiner, Art Unit 2655
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